Improving the quality of pain management is a high priority for the Veterans Health Administration (VHA). VHA has published policy guidance that establishes an innovative stepped care model of pain management (SCM- PM) as the single standard of pain care. The SCM-PM provides the ability to assess and treat pain in primary care settings, while maintaining the capacity to escalate treatment options to include specialized care, if necessary. The model further emphasizes the importance of an individually tailored, integrated, multi-modal approach to pain management that takes into account mental health comorbidities and that incorporates complementary health approaches (CHA) to promote optimal pain control and improved function and quality of life. Despite great strides in promoting this model, pain management performance improvement efforts have been hampered by the limited availability of reliable Pain Care Quality indicators and metrics for key dimensions of pai management in order to promote their utilization in systematic quality improvement efforts. Other than pharmacological and procedure based interventions in which specific, easily retrievable codes are used to document care in the VHA's electronic health record (EHR), it is difficult to capture the broader array of CHA or key aspects of integrated care. These gaps in the EHR and VHA database pose serious barriers to promoting performance improvement efforts including implementation of the SCM-PM. The proposed project extends prior research by our investigator team by using Natural Language Processing (NLP) and Machine Learning (ML) to automate a previously validated approach to identify and quantify key dimensions of Pain Care Quality, namely assessment, especially functional assessment, integrated treatment plans, reassessment (outcomes), and patient education from the EHR. Once this automated solution is validated, we intend to apply it to a national sample to test important questions about Pain Care Quality among veterans with comorbid mental health conditions, access to CHA, and the SCM-PM. This innovative solution to identifying key dimensions of healthcare has potential applicability to improving the management of other complex health problems for which existing quality of care indicators and metrics are limited.